Ornus Capital Project 1 by Jaspreet Kang¶
In [26]:
# %load crypto_extract.py
from bs4 import BeautifulSoup
import requests
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import statsmodels.api as sm
import statsmodels.formula.api as smf
### Testing function: function for one coin that returns just pct change
def test_coin(beg_date, end_date, coin_name):
url = "https://coinmarketcap.com/currencies/" + str(coin_name) + "/historical-data/?start=" + str(beg_date) + "&end=" + str(end_date)
content = requests.get(url).content
soup = BeautifulSoup(content,'html.parser')
table = soup.find('table', {'class': 'table'})
data = [[td.text.strip() for td in tr.findChildren('td')]
for tr in table.findChildren('tr')]
df = pd.DataFrame(data)
df.drop(df.index[0], inplace=True) # first row is empty
df[0] = pd.to_datetime(df[0]) # date
for i in range(1,7):
df[i] = pd.to_numeric(df[i].str.replace(",","").str.replace("-","")) # some vol is missing and has -
df.columns = ['Date','Open','High','Low','Close','Volume','Market Cap']
df.set_index('Date',inplace=True)
df.sort_index(inplace=True)
df['Price'] = (df['Open'] + df['Close']) / 2
df[str(coin_name) + '%'] = df['Price'].pct_change()
df_pct = pd.DataFrame(df[str(coin_name) + '%'])
return df_pct
#### everything function: function that inputs beginning date, end date, and x number of
#### coins you'd like to inspect. Calling function will spit out graphs and important
#### information
def everything1(date1, date2, arg, *args):
d = test_coin(date1, date2, arg)
for coin in args:
df = test_coin(date1, date2, coin)
d = pd.concat([d, df], axis=1, join='inner')
if (len(d.columns) == 2):
corr = d.corr()
print(corr); print(corr**2)
model = smf.ols('d[d.columns[1]] ~ d[d.columns[0]]', data=d, missing='drop').fit()
print(model.summary())
#d_norm = d.divide(d.ix[1])# a lot of coins have different starting dates. thats why we index 2 row instead of 1st
plt.ion(); plt.figure()
#d_norm.plot(figsize = (15,10))
d.plot(figsize = (15,10))
plt.pause(1); plt.figure(figsize = (10, 10))
sns.regplot(x=d[d.columns[0]], y=d[d.columns[1]], data=d)
plt.ioff(); plt.show()
else:
corr = d.corr()
print(corr); print(corr**2)
d_norm = d.divide(d.ix[1])
plt.ion(); plt.figure()
d_norm.plot(figsize = (15, 10))
plt.pause(1)
plt.figure(figsize=(10,10))
sns.heatmap(corr, xticklabels=corr.columns.values, yticklabels=corr.columns.values)
plt.ioff(); plt.show()
In [9]:
from crypto_extract import everything1
Ethereum ~ Bitcoin (Year to Date)¶
In [27]:
everything1(20190101, 20190421, "bitcoin", "ethereum")
bitcoin% ethereum%
bitcoin% 1.000000 0.859784
ethereum% 0.859784 1.000000
bitcoin% ethereum%
bitcoin% 1.000000 0.739229
ethereum% 0.739229 1.000000
OLS Regression Results
==============================================================================
Dep. Variable: d[d.columns[1]] R-squared: 0.739
Model: OLS Adj. R-squared: 0.737
Method: Least Squares F-statistic: 306.2
Date: Mon, 22 Apr 2019 Prob (F-statistic): 2.67e-33
Time: 16:49:25 Log-Likelihood: 313.07
No. Observations: 110 AIC: -622.1
Df Residuals: 108 BIC: -616.7
Df Model: 1
Covariance Type: nonrobust
===================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------
Intercept -0.0018 0.001 -1.308 0.194 -0.005 0.001
d[d.columns[0]] 1.3031 0.074 17.497 0.000 1.155 1.451
==============================================================================
Omnibus: 13.703 Durbin-Watson: 0.835
Prob(Omnibus): 0.001 Jarque-Bera (JB): 45.914
Skew: -0.111 Prob(JB): 1.07e-10
Kurtosis: 6.157 Cond. No. 55.1
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
<Figure size 432x288 with 0 Axes>
Ethereum ~ Bitcoin (1 Year)¶
In [28]:
everything1(20180421, 20190421, "bitcoin", "ethereum")
bitcoin% ethereum%
bitcoin% 1.000000 0.842086
ethereum% 0.842086 1.000000
bitcoin% ethereum%
bitcoin% 1.000000 0.709109
ethereum% 0.709109 1.000000
OLS Regression Results
==============================================================================
Dep. Variable: d[d.columns[1]] R-squared: 0.709
Model: OLS Adj. R-squared: 0.708
Method: Least Squares F-statistic: 884.9
Date: Mon, 22 Apr 2019 Prob (F-statistic): 2.31e-99
Time: 16:49:35 Log-Likelihood: 948.62
No. Observations: 365 AIC: -1893.
Df Residuals: 363 BIC: -1885.
Df Model: 1
Covariance Type: nonrobust
===================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------
Intercept -0.0014 0.001 -1.510 0.132 -0.003 0.000
d[d.columns[0]] 1.2796 0.043 29.747 0.000 1.195 1.364
==============================================================================
Omnibus: 39.844 Durbin-Watson: 0.895
Prob(Omnibus): 0.000 Jarque-Bera (JB): 209.817
Skew: 0.202 Prob(JB): 2.75e-46
Kurtosis: 6.692 Cond. No. 45.6
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
<Figure size 432x288 with 0 Axes>
Ethereum ~ Bitcoin (3 Years)¶
In [29]:
everything1(20160421, 20190421, "bitcoin", "ethereum")
bitcoin% ethereum%
bitcoin% 1.000000 0.466272
ethereum% 0.466272 1.000000
bitcoin% ethereum%
bitcoin% 1.00000 0.21741
ethereum% 0.21741 1.00000
OLS Regression Results
==============================================================================
Dep. Variable: d[d.columns[1]] R-squared: 0.217
Model: OLS Adj. R-squared: 0.217
Method: Least Squares F-statistic: 303.6
Date: Mon, 22 Apr 2019 Prob (F-statistic): 3.38e-60
Time: 16:51:08 Log-Likelihood: 2012.0
No. Observations: 1095 AIC: -4020.
Df Residuals: 1093 BIC: -4010.
Df Model: 1
Covariance Type: nonrobust
===================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------
Intercept 0.0018 0.001 1.560 0.119 -0.000 0.004
d[d.columns[0]] 0.6995 0.040 17.425 0.000 0.621 0.778
==============================================================================
Omnibus: 323.057 Durbin-Watson: 0.930
Prob(Omnibus): 0.000 Jarque-Bera (JB): 3132.562
Skew: 1.068 Prob(JB): 0.00
Kurtosis: 11.006 Cond. No. 34.4
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
<Figure size 432x288 with 0 Axes>
C:\ProgramData\Anaconda3\lib\site-packages\scipy\stats\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result. return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
Ethereum ~ Bitcoin (August 7, 2015 to Date)¶
In [30]:
everything1(20150807, 20190421, "bitcoin", "ethereum")
bitcoin% ethereum%
bitcoin% 1.00000 0.35483
ethereum% 0.35483 1.00000
bitcoin% ethereum%
bitcoin% 1.000000 0.125904
ethereum% 0.125904 1.000000
OLS Regression Results
==============================================================================
Dep. Variable: d[d.columns[1]] R-squared: 0.126
Model: OLS Adj. R-squared: 0.125
Method: Least Squares F-statistic: 194.6
Date: Mon, 22 Apr 2019 Prob (F-statistic): 2.03e-41
Time: 16:52:42 Log-Likelihood: 2138.3
No. Observations: 1353 AIC: -4273.
Df Residuals: 1351 BIC: -4262.
Df Model: 1
Covariance Type: nonrobust
===================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------
Intercept 0.0028 0.001 2.055 0.040 0.000 0.005
d[d.columns[0]] 0.6829 0.049 13.950 0.000 0.587 0.779
==============================================================================
Omnibus: 371.571 Durbin-Watson: 0.881
Prob(Omnibus): 0.000 Jarque-Bera (JB): 24954.817
Skew: -0.286 Prob(JB): 0.00
Kurtosis: 24.032 Cond. No. 36.1
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
<Figure size 432x288 with 0 Axes>
Ontology ~ Bitcoin (Year to Date)¶
In [31]:
everything1(20190101, 20190421, "bitcoin", "ontology")
bitcoin% ontology%
bitcoin% 1.000000 0.473876
ontology% 0.473876 1.000000
bitcoin% ontology%
bitcoin% 1.000000 0.224558
ontology% 0.224558 1.000000
OLS Regression Results
==============================================================================
Dep. Variable: d[d.columns[1]] R-squared: 0.225
Model: OLS Adj. R-squared: 0.217
Method: Least Squares F-statistic: 31.28
Date: Mon, 22 Apr 2019 Prob (F-statistic): 1.70e-07
Time: 16:54:20 Log-Likelihood: 223.55
No. Observations: 110 AIC: -443.1
Df Residuals: 108 BIC: -437.7
Df Model: 1
Covariance Type: nonrobust
===================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------
Intercept 0.0044 0.003 1.432 0.155 -0.002 0.011
d[d.columns[0]] 0.9398 0.168 5.592 0.000 0.607 1.273
==============================================================================
Omnibus: 38.272 Durbin-Watson: 1.086
Prob(Omnibus): 0.000 Jarque-Bera (JB): 76.155
Skew: 1.437 Prob(JB): 2.90e-17
Kurtosis: 5.891 Cond. No. 55.1
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
<Figure size 432x288 with 0 Axes>
Ontology ~ Bitcoin (March 8, 2018 to Date)¶
In [32]:
everything1(20180308, 20190421, "bitcoin", "ontology")
bitcoin% ontology%
bitcoin% 1.00000 0.45525
ontology% 0.45525 1.00000
bitcoin% ontology%
bitcoin% 1.000000 0.207253
ontology% 0.207253 1.000000
OLS Regression Results
==============================================================================
Dep. Variable: d[d.columns[1]] R-squared: 0.207
Model: OLS Adj. R-squared: 0.205
Method: Least Squares F-statistic: 106.4
Date: Mon, 22 Apr 2019 Prob (F-statistic): 2.56e-22
Time: 16:56:03 Log-Likelihood: 609.91
No. Observations: 409 AIC: -1216.
Df Residuals: 407 BIC: -1208.
Df Model: 1
Covariance Type: nonrobust
===================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------
Intercept 0.0014 0.003 0.503 0.615 -0.004 0.007
d[d.columns[0]] 1.2018 0.117 10.315 0.000 0.973 1.431
==============================================================================
Omnibus: 241.661 Durbin-Watson: 0.961
Prob(Omnibus): 0.000 Jarque-Bera (JB): 3983.517
Skew: 2.161 Prob(JB): 0.00
Kurtosis: 17.665 Cond. No. 43.2
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
<Figure size 432x288 with 0 Axes>
VeChain ~ Bitcoin (Year to Date)¶
In [33]:
everything1(20190101, 20190421, "bitcoin", "vechain")
bitcoin% vechain%
bitcoin% 1.000000 0.681983
vechain% 0.681983 1.000000
bitcoin% vechain%
bitcoin% 1.0000 0.4651
vechain% 0.4651 1.0000
OLS Regression Results
==============================================================================
Dep. Variable: d[d.columns[1]] R-squared: 0.465
Model: OLS Adj. R-squared: 0.460
Method: Least Squares F-statistic: 93.91
Date: Mon, 22 Apr 2019 Prob (F-statistic): 2.36e-16
Time: 16:57:12 Log-Likelihood: 272.48
No. Observations: 110 AIC: -541.0
Df Residuals: 108 BIC: -535.6
Df Model: 1
Covariance Type: nonrobust
===================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------
Intercept 0.0018 0.002 0.931 0.354 -0.002 0.006
d[d.columns[0]] 1.0439 0.108 9.691 0.000 0.830 1.257
==============================================================================
Omnibus: 27.169 Durbin-Watson: 1.286
Prob(Omnibus): 0.000 Jarque-Bera (JB): 43.649
Skew: 1.115 Prob(JB): 3.32e-10
Kurtosis: 5.133 Cond. No. 55.1
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
<Figure size 432x288 with 0 Axes>
Vechain ~ Bitcoin (August 3, 2018 to Date)¶
In [34]:
everything1(20180803, 20190421, "bitcoin", "vechain")
bitcoin% vechain%
bitcoin% 1.00000 0.69574
vechain% 0.69574 1.00000
bitcoin% vechain%
bitcoin% 1.000000 0.484054
vechain% 0.484054 1.000000
OLS Regression Results
==============================================================================
Dep. Variable: d[d.columns[1]] R-squared: 0.484
Model: OLS Adj. R-squared: 0.482
Method: Least Squares F-statistic: 243.0
Date: Mon, 22 Apr 2019 Prob (F-statistic): 4.29e-39
Time: 16:58:30 Log-Likelihood: 518.52
No. Observations: 261 AIC: -1033.
Df Residuals: 259 BIC: -1026.
Df Model: 1
Covariance Type: nonrobust
===================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------
Intercept -0.0004 0.002 -0.190 0.849 -0.004 0.004
d[d.columns[0]] 1.4837 0.095 15.588 0.000 1.296 1.671
==============================================================================
Omnibus: 309.606 Durbin-Watson: 0.967
Prob(Omnibus): 0.000 Jarque-Bera (JB): 24426.700
Skew: 4.921 Prob(JB): 0.00
Kurtosis: 49.360 Cond. No. 46.2
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
<Figure size 432x288 with 0 Axes>
Nano ~ Bitcoin (Year to Date)¶
In [35]:
everything1(20190101, 20190421, "bitcoin", "nano")
bitcoin% nano%
bitcoin% 1.000000 0.729099
nano% 0.729099 1.000000
bitcoin% nano%
bitcoin% 1.000000 0.531585
nano% 0.531585 1.000000
OLS Regression Results
==============================================================================
Dep. Variable: d[d.columns[1]] R-squared: 0.532
Model: OLS Adj. R-squared: 0.527
Method: Least Squares F-statistic: 122.6
Date: Mon, 22 Apr 2019 Prob (F-statistic): 1.71e-19
Time: 16:59:24 Log-Likelihood: 263.99
No. Observations: 110 AIC: -524.0
Df Residuals: 108 BIC: -518.6
Df Model: 1
Covariance Type: nonrobust
===================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------
Intercept 0.0018 0.002 0.842 0.401 -0.002 0.006
d[d.columns[0]] 1.2882 0.116 11.071 0.000 1.058 1.519
==============================================================================
Omnibus: 20.421 Durbin-Watson: 1.427
Prob(Omnibus): 0.000 Jarque-Bera (JB): 34.312
Skew: 0.814 Prob(JB): 3.54e-08
Kurtosis: 5.199 Cond. No. 55.1
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
<Figure size 432x288 with 0 Axes>
Nano ~ Bitcoin (1 Year)¶
In [36]:
everything1(20180421, 20190421, "bitcoin", "nano")
bitcoin% nano%
bitcoin% 1.000000 0.682925
nano% 0.682925 1.000000
bitcoin% nano%
bitcoin% 1.000000 0.466386
nano% 0.466386 1.000000
OLS Regression Results
==============================================================================
Dep. Variable: d[d.columns[1]] R-squared: 0.466
Model: OLS Adj. R-squared: 0.465
Method: Least Squares F-statistic: 317.3
Date: Mon, 22 Apr 2019 Prob (F-statistic): 1.90e-51
Time: 17:00:30 Log-Likelihood: 702.47
No. Observations: 365 AIC: -1401.
Df Residuals: 363 BIC: -1393.
Df Model: 1
Covariance Type: nonrobust
===================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------
Intercept -0.0011 0.002 -0.597 0.551 -0.005 0.003
d[d.columns[0]] 1.5039 0.084 17.812 0.000 1.338 1.670
==============================================================================
Omnibus: 206.675 Durbin-Watson: 0.947
Prob(Omnibus): 0.000 Jarque-Bera (JB): 2293.238
Skew: 2.149 Prob(JB): 0.00
Kurtosis: 14.503 Cond. No. 45.6
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
<Figure size 432x288 with 0 Axes>
Nano ~ Bitcoin (March 6, 2017 to Date)¶
In [37]:
everything1(20170306, 20190421, "bitcoin", "nano")
bitcoin% nano%
bitcoin% 1.000000 0.384218
nano% 0.384218 1.000000
bitcoin% nano%
bitcoin% 1.000000 0.147623
nano% 0.147623 1.000000
OLS Regression Results
==============================================================================
Dep. Variable: d[d.columns[1]] R-squared: 0.148
Model: OLS Adj. R-squared: 0.146
Method: Least Squares F-statistic: 131.3
Date: Mon, 22 Apr 2019 Prob (F-statistic): 3.83e-28
Time: 17:02:12 Log-Likelihood: 811.15
No. Observations: 760 AIC: -1618.
Df Residuals: 758 BIC: -1609.
Df Model: 1
Covariance Type: nonrobust
===================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------
Intercept 0.0077 0.003 2.555 0.011 0.002 0.014
d[d.columns[0]] 1.0923 0.095 11.458 0.000 0.905 1.279
==============================================================================
Omnibus: 431.922 Durbin-Watson: 0.879
Prob(Omnibus): 0.000 Jarque-Bera (JB): 4399.122
Skew: 2.389 Prob(JB): 0.00
Kurtosis: 13.775 Cond. No. 31.5
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
<Figure size 432x288 with 0 Axes>
Decred ~ Bitcoin (Year to Date)¶
In [38]:
everything1(20190101, 20190421, "bitcoin", "decred")
bitcoin% decred%
bitcoin% 1.00000 0.78131
decred% 0.78131 1.00000
bitcoin% decred%
bitcoin% 1.000000 0.610445
decred% 0.610445 1.000000
OLS Regression Results
==============================================================================
Dep. Variable: d[d.columns[1]] R-squared: 0.610
Model: OLS Adj. R-squared: 0.607
Method: Least Squares F-statistic: 169.2
Date: Mon, 22 Apr 2019 Prob (F-statistic): 7.58e-24
Time: 17:03:37 Log-Likelihood: 309.89
No. Observations: 110 AIC: -615.8
Df Residuals: 108 BIC: -610.4
Df Model: 1
Covariance Type: nonrobust
===================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------
Intercept 0.0007 0.001 0.503 0.616 -0.002 0.004
d[d.columns[0]] 0.9973 0.077 13.009 0.000 0.845 1.149
==============================================================================
Omnibus: 19.020 Durbin-Watson: 1.145
Prob(Omnibus): 0.000 Jarque-Bera (JB): 32.876
Skew: 0.745 Prob(JB): 7.26e-08
Kurtosis: 5.226 Cond. No. 55.1
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
<Figure size 432x288 with 0 Axes>
Decred ~ Bitcoin (1 Year)¶
In [39]:
everything1(20180421, 20190421, "bitcoin", "decred")
bitcoin% decred%
bitcoin% 1.000000 0.693335
decred% 0.693335 1.000000
bitcoin% decred%
bitcoin% 1.000000 0.480713
decred% 0.480713 1.000000
OLS Regression Results
==============================================================================
Dep. Variable: d[d.columns[1]] R-squared: 0.481
Model: OLS Adj. R-squared: 0.479
Method: Least Squares F-statistic: 336.0
Date: Mon, 22 Apr 2019 Prob (F-statistic): 1.34e-53
Time: 17:04:54 Log-Likelihood: 820.58
No. Observations: 365 AIC: -1637.
Df Residuals: 363 BIC: -1629.
Df Model: 1
Covariance Type: nonrobust
===================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------
Intercept -0.0007 0.001 -0.532 0.595 -0.003 0.002
d[d.columns[0]] 1.1198 0.061 18.331 0.000 1.000 1.240
==============================================================================
Omnibus: 128.025 Durbin-Watson: 1.238
Prob(Omnibus): 0.000 Jarque-Bera (JB): 956.491
Skew: 1.264 Prob(JB): 2.00e-208
Kurtosis: 10.517 Cond. No. 45.6
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
<Figure size 432x288 with 0 Axes>
Decred ~ Bitcoin (3 Years)¶
In [40]:
everything1(20160421, 20190421, "bitcoin", "decred")
bitcoin% decred%
bitcoin% 1.000000 0.365403
decred% 0.365403 1.000000
bitcoin% decred%
bitcoin% 1.00000 0.13352
decred% 0.13352 1.00000
OLS Regression Results
==============================================================================
Dep. Variable: d[d.columns[1]] R-squared: 0.134
Model: OLS Adj. R-squared: 0.133
Method: Least Squares F-statistic: 168.4
Date: Mon, 22 Apr 2019 Prob (F-statistic): 6.34e-36
Time: 17:05:43 Log-Likelihood: 1694.8
No. Observations: 1095 AIC: -3386.
Df Residuals: 1093 BIC: -3376.
Df Model: 1
Covariance Type: nonrobust
===================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------
Intercept 0.0021 0.002 1.374 0.170 -0.001 0.005
d[d.columns[0]] 0.6960 0.054 12.978 0.000 0.591 0.801
==============================================================================
Omnibus: 493.479 Durbin-Watson: 1.236
Prob(Omnibus): 0.000 Jarque-Bera (JB): 4680.425
Skew: 1.829 Prob(JB): 0.00
Kurtosis: 12.445 Cond. No. 34.4
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
<Figure size 432x288 with 0 Axes>
C:\ProgramData\Anaconda3\lib\site-packages\scipy\stats\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result. return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
EOS ~ Bitcoin (Year to Date)¶
In [41]:
everything1(20190101, 20190421, "bitcoin", "eos")
bitcoin% eos%
bitcoin% 1.000000 0.852096
eos% 0.852096 1.000000
bitcoin% eos%
bitcoin% 1.000000 0.726067
eos% 0.726067 1.000000
OLS Regression Results
==============================================================================
Dep. Variable: d[d.columns[1]] R-squared: 0.726
Model: OLS Adj. R-squared: 0.724
Method: Least Squares F-statistic: 286.3
Date: Mon, 22 Apr 2019 Prob (F-statistic): 3.85e-32
Time: 17:06:49 Log-Likelihood: 284.69
No. Observations: 110 AIC: -565.4
Df Residuals: 108 BIC: -560.0
Df Model: 1
Covariance Type: nonrobust
===================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------
Intercept 0.0018 0.002 1.037 0.302 -0.002 0.005
d[d.columns[0]] 1.6310 0.096 16.919 0.000 1.440 1.822
==============================================================================
Omnibus: 14.255 Durbin-Watson: 1.004
Prob(Omnibus): 0.001 Jarque-Bera (JB): 20.001
Skew: 0.643 Prob(JB): 4.54e-05
Kurtosis: 4.646 Cond. No. 55.1
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
<Figure size 432x288 with 0 Axes>
EOS ~ Bitcoin (1 Year)¶
In [42]:
everything1(20180421, 20190421, "bitcoin", "eos")
bitcoin% eos%
bitcoin% 1.000000 0.771295
eos% 0.771295 1.000000
bitcoin% eos%
bitcoin% 1.000000 0.594896
eos% 0.594896 1.000000
OLS Regression Results
==============================================================================
Dep. Variable: d[d.columns[1]] R-squared: 0.595
Model: OLS Adj. R-squared: 0.594
Method: Least Squares F-statistic: 533.1
Date: Mon, 22 Apr 2019 Prob (F-statistic): 3.21e-73
Time: 17:07:56 Log-Likelihood: 795.91
No. Observations: 365 AIC: -1588.
Df Residuals: 363 BIC: -1580.
Df Model: 1
Covariance Type: nonrobust
===================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------
Intercept 0.0006 0.001 0.423 0.672 -0.002 0.003
d[d.columns[0]] 1.5090 0.065 23.088 0.000 1.381 1.638
==============================================================================
Omnibus: 39.336 Durbin-Watson: 0.946
Prob(Omnibus): 0.000 Jarque-Bera (JB): 102.445
Skew: 0.510 Prob(JB): 5.68e-23
Kurtosis: 5.387 Cond. No. 45.6
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
<Figure size 432x288 with 0 Axes>
EOS ~ Bitcoin (July 1, 2017 to Date)¶
In [43]:
everything1(20170701, 20190421, "bitcoin", "eos")
bitcoin% eos%
bitcoin% 1.000000 0.469207
eos% 0.469207 1.000000
bitcoin% eos%
bitcoin% 1.000000 0.220155
eos% 0.220155 1.000000
OLS Regression Results
==============================================================================
Dep. Variable: d[d.columns[1]] R-squared: 0.220
Model: OLS Adj. R-squared: 0.219
Method: Least Squares F-statistic: 185.5
Date: Mon, 22 Apr 2019 Prob (F-statistic): 2.21e-37
Time: 17:08:59 Log-Likelihood: 846.03
No. Observations: 659 AIC: -1688.
Df Residuals: 657 BIC: -1679.
Df Model: 1
Covariance Type: nonrobust
===================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------
Intercept 0.0031 0.003 1.198 0.231 -0.002 0.008
d[d.columns[0]] 1.0970 0.081 13.619 0.000 0.939 1.255
==============================================================================
Omnibus: 799.045 Durbin-Watson: 0.779
Prob(Omnibus): 0.000 Jarque-Bera (JB): 118756.010
Skew: 5.782 Prob(JB): 0.00
Kurtosis: 67.740 Cond. No. 30.8
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
<Figure size 432x288 with 0 Axes>
NEO ~ Bitcoin (Year to Date)¶
In [44]:
everything1(20190101, 20190421, "bitcoin", "neo")
bitcoin% neo%
bitcoin% 1.000000 0.778159
neo% 0.778159 1.000000
bitcoin% neo%
bitcoin% 1.000000 0.605531
neo% 0.605531 1.000000
OLS Regression Results
==============================================================================
Dep. Variable: d[d.columns[1]] R-squared: 0.606
Model: OLS Adj. R-squared: 0.602
Method: Least Squares F-statistic: 165.8
Date: Mon, 22 Apr 2019 Prob (F-statistic): 1.50e-23
Time: 17:09:59 Log-Likelihood: 286.19
No. Observations: 110 AIC: -568.4
Df Residuals: 108 BIC: -563.0
Df Model: 1
Covariance Type: nonrobust
===================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------
Intercept -0.0006 0.002 -0.317 0.752 -0.004 0.003
d[d.columns[0]] 1.2244 0.095 12.876 0.000 1.036 1.413
==============================================================================
Omnibus: 14.670 Durbin-Watson: 1.063
Prob(Omnibus): 0.001 Jarque-Bera (JB): 17.655
Skew: 0.749 Prob(JB): 0.000147
Kurtosis: 4.267 Cond. No. 55.1
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
<Figure size 432x288 with 0 Axes>
NEO ~ Bitcoin (1 Year)¶
In [45]:
everything1(20180421, 20190421, "bitcoin", "neo")
bitcoin% neo%
bitcoin% 1.000000 0.796365
neo% 0.796365 1.000000
bitcoin% neo%
bitcoin% 1.000000 0.634198
neo% 0.634198 1.000000
OLS Regression Results
==============================================================================
Dep. Variable: d[d.columns[1]] R-squared: 0.634
Model: OLS Adj. R-squared: 0.633
Method: Least Squares F-statistic: 629.3
Date: Mon, 22 Apr 2019 Prob (F-statistic): 2.81e-81
Time: 17:10:51 Log-Likelihood: 859.81
No. Observations: 365 AIC: -1716.
Df Residuals: 363 BIC: -1708.
Df Model: 1
Covariance Type: nonrobust
===================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------
Intercept -0.0031 0.001 -2.548 0.011 -0.005 -0.001
d[d.columns[0]] 1.3763 0.055 25.087 0.000 1.268 1.484
==============================================================================
Omnibus: 62.809 Durbin-Watson: 1.068
Prob(Omnibus): 0.000 Jarque-Bera (JB): 230.807
Skew: 0.706 Prob(JB): 7.60e-51
Kurtosis: 6.631 Cond. No. 45.6
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
<Figure size 432x288 with 0 Axes>
NEO ~ Bitcoin (September 8, 2016 to Date)¶
In [46]:
everything1(20160908, 20190421, "bitcoin", "neo")
bitcoin% neo%
bitcoin% 1.000000 0.347775
neo% 0.347775 1.000000
bitcoin% neo%
bitcoin% 1.000000 0.120947
neo% 0.120947 1.000000
OLS Regression Results
==============================================================================
Dep. Variable: d[d.columns[1]] R-squared: 0.121
Model: OLS Adj. R-squared: 0.120
Method: Least Squares F-statistic: 131.0
Date: Mon, 22 Apr 2019 Prob (F-statistic): 1.66e-28
Time: 17:12:24 Log-Likelihood: 1160.2
No. Observations: 954 AIC: -2316.
Df Residuals: 952 BIC: -2307.
Df Model: 1
Covariance Type: nonrobust
===================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------
Intercept 0.0038 0.002 1.610 0.108 -0.001 0.008
d[d.columns[0]] 0.8884 0.078 11.445 0.000 0.736 1.041
==============================================================================
Omnibus: 838.096 Durbin-Watson: 1.077
Prob(Omnibus): 0.000 Jarque-Bera (JB): 53962.006
Skew: 3.647 Prob(JB): 0.00
Kurtosis: 39.116 Cond. No. 33.4
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
<Figure size 432x288 with 0 Axes>
C:\ProgramData\Anaconda3\lib\site-packages\scipy\stats\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result. return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
Icon ~ Bitcoin (Year to Date)¶
In [47]:
everything1(20190101, 20190421, "bitcoin", "icon")
bitcoin% icon%
bitcoin% 1.000000 0.606626
icon% 0.606626 1.000000
bitcoin% icon%
bitcoin% 1.000000 0.367995
icon% 0.367995 1.000000
OLS Regression Results
==============================================================================
Dep. Variable: d[d.columns[1]] R-squared: 0.368
Model: OLS Adj. R-squared: 0.362
Method: Least Squares F-statistic: 62.88
Date: Mon, 22 Apr 2019 Prob (F-statistic): 2.16e-12
Time: 17:13:03 Log-Likelihood: 245.53
No. Observations: 110 AIC: -487.1
Df Residuals: 108 BIC: -481.7
Df Model: 1
Covariance Type: nonrobust
===================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------
Intercept 0.0009 0.003 0.348 0.729 -0.004 0.006
d[d.columns[0]] 1.0913 0.138 7.930 0.000 0.819 1.364
==============================================================================
Omnibus: 9.071 Durbin-Watson: 1.111
Prob(Omnibus): 0.011 Jarque-Bera (JB): 8.949
Skew: 0.609 Prob(JB): 0.0114
Kurtosis: 3.684 Cond. No. 55.1
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
<Figure size 432x288 with 0 Axes>
Icon ~ Bitcoin (1 Year)¶
In [48]:
everything1(20180421, 20190421, "bitcoin", "icon")
bitcoin% icon%
bitcoin% 1.000000 0.727937
icon% 0.727937 1.000000
bitcoin% icon%
bitcoin% 1.000000 0.529893
icon% 0.529893 1.000000
OLS Regression Results
==============================================================================
Dep. Variable: d[d.columns[1]] R-squared: 0.530
Model: OLS Adj. R-squared: 0.529
Method: Least Squares F-statistic: 409.2
Date: Mon, 22 Apr 2019 Prob (F-statistic): 1.83e-61
Time: 17:14:06 Log-Likelihood: 759.59
No. Observations: 365 AIC: -1515.
Df Residuals: 363 BIC: -1507.
Df Model: 1
Covariance Type: nonrobust
===================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------
Intercept -0.0035 0.002 -2.186 0.029 -0.007 -0.000
d[d.columns[0]] 1.4604 0.072 20.228 0.000 1.318 1.602
==============================================================================
Omnibus: 106.545 Durbin-Watson: 1.189
Prob(Omnibus): 0.000 Jarque-Bera (JB): 671.614
Skew: 1.061 Prob(JB): 1.45e-146
Kurtosis: 9.298 Cond. No. 45.6
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
<Figure size 432x288 with 0 Axes>
Icon ~ Bitcoin (October 26, 2017 to Date)¶
In [49]:
everything1(20171026, 20190421, "bitcoin", "icon")
bitcoin% icon%
bitcoin% 1.000000 0.528981
icon% 0.528981 1.000000
bitcoin% icon%
bitcoin% 1.000000 0.279821
icon% 0.279821 1.000000
OLS Regression Results
==============================================================================
Dep. Variable: d[d.columns[1]] R-squared: 0.280
Model: OLS Adj. R-squared: 0.278
Method: Least Squares F-statistic: 209.4
Date: Mon, 22 Apr 2019 Prob (F-statistic): 2.46e-40
Time: 17:15:20 Log-Likelihood: 779.31
No. Observations: 541 AIC: -1555.
Df Residuals: 539 BIC: -1546.
Df Model: 1
Covariance Type: nonrobust
===================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------
Intercept 0.0014 0.002 0.586 0.558 -0.003 0.006
d[d.columns[0]] 1.1285 0.078 14.472 0.000 0.975 1.282
==============================================================================
Omnibus: 271.080 Durbin-Watson: 1.018
Prob(Omnibus): 0.000 Jarque-Bera (JB): 2481.644
Skew: 1.997 Prob(JB): 0.00
Kurtosis: 12.703 Cond. No. 31.6
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
<Figure size 432x288 with 0 Axes>
Looking at 9 Coins of Interest: Bitcoin, Ethereum, Ontology, Vechain, Nano, Decred, EOS, NEO, Icon¶
In [50]:
everything1(20190101, 20190421, "bitcoin", "ethereum", "ontology", "vechain", "nano", "decred", "eos", "neo", "icon")
bitcoin% ethereum% ontology% vechain% nano% decred% \
bitcoin% 1.000000 0.859784 0.473876 0.681983 0.729099 0.781310
ethereum% 0.859784 1.000000 0.468865 0.637721 0.655482 0.750873
ontology% 0.473876 0.468865 1.000000 0.563568 0.370159 0.393424
vechain% 0.681983 0.637721 0.563568 1.000000 0.640685 0.694117
nano% 0.729099 0.655482 0.370159 0.640685 1.000000 0.676924
decred% 0.781310 0.750873 0.393424 0.694117 0.676924 1.000000
eos% 0.852096 0.861508 0.507485 0.618089 0.638419 0.686650
neo% 0.778159 0.718259 0.664069 0.659053 0.573678 0.672888
icon% 0.606626 0.633095 0.450835 0.643517 0.486578 0.588613
eos% neo% icon%
bitcoin% 0.852096 0.778159 0.606626
ethereum% 0.861508 0.718259 0.633095
ontology% 0.507485 0.664069 0.450835
vechain% 0.618089 0.659053 0.643517
nano% 0.638419 0.573678 0.486578
decred% 0.686650 0.672888 0.588613
eos% 1.000000 0.739668 0.555314
neo% 0.739668 1.000000 0.618044
icon% 0.555314 0.618044 1.000000
bitcoin% ethereum% ontology% vechain% nano% decred% \
bitcoin% 1.000000 0.739229 0.224558 0.465100 0.531585 0.610445
ethereum% 0.739229 1.000000 0.219834 0.406689 0.429657 0.563810
ontology% 0.224558 0.219834 1.000000 0.317609 0.137018 0.154783
vechain% 0.465100 0.406689 0.317609 1.000000 0.410478 0.481799
nano% 0.531585 0.429657 0.137018 0.410478 1.000000 0.458226
decred% 0.610445 0.563810 0.154783 0.481799 0.458226 1.000000
eos% 0.726067 0.742196 0.257541 0.382034 0.407579 0.471488
neo% 0.605531 0.515895 0.440987 0.434351 0.329106 0.452779
icon% 0.367995 0.400809 0.203252 0.414114 0.236758 0.346465
eos% neo% icon%
bitcoin% 0.726067 0.605531 0.367995
ethereum% 0.742196 0.515895 0.400809
ontology% 0.257541 0.440987 0.203252
vechain% 0.382034 0.434351 0.414114
nano% 0.407579 0.329106 0.236758
decred% 0.471488 0.452779 0.346465
eos% 1.000000 0.547109 0.308374
neo% 0.547109 1.000000 0.381979
icon% 0.308374 0.381979 1.000000
C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:57: DeprecationWarning: .ix is deprecated. Please use .loc for label based indexing or .iloc for positional indexing See the documentation here: http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated
<Figure size 432x288 with 0 Axes>
In [ ]: